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Although acoustic scenes and events include many related tasks, theircombined detection and classification have been scarcely investigated. Wepropose three architectures of deep neural networks that are integrated tosimultaneously perform acoustic scene classification, audio tagging, and soundevent detection. The first two architectures are inspired by human cognitiveprocesses. The first architecture resembles the short-term perception for sceneclassification of adults, who can detect various sound events that are thenused to identify the acoustic scene. The second architecture resembles thelong-term learning of babies, being also the concept underlying self-supervisedlearning. Babies first observe the effects of abstract notions such as gravityand then learn specific tasks using such perceptions. The third architectureadds a few layers to the second one that solely perform a single task beforeits corresponding output layer. We aim to build an integrated system that canserve as a pretrained model to perform the three abovementioned tasks.Experiments on three datasets demonstrate that the proposed architecture,called DcaseNet, can be either directly used for any of the tasks whileproviding suitable results or fine-tuned to improve the performance of onetask. The code and pretrained DcaseNet weights are available athttps://github.com/Jungjee/DcaseNet.

DcaseNet: An integrated pretrained deep neural network for detecting and classifying acoustic scenes and events